Google Cloud Capacity Constraints (2026): What It Means For You

Every major cloud is short on capacity right now. Microsoft CFO Amy Hood told investors in July that "customer demand continues to exceed available capacity." Amazon CEO Andy Jassy said AWS still has "capacity constraints that yield unserved demand." And Alphabet CEO Sundar Pichai said Google is "compute constrained in the near-term."

Google Cloud is the fastest-growing of the three, with revenue up 82% year over year last quarter, and its customers are feeling that demand. VM requests and GKE scale-ups have been returning ZONE_RESOURCE_POOL_EXHAUSTED on everyday machine types like e2, n1 and n2. If that's happened to you, you're not alone.

No matter what cloud you're on, heading into the holiday season, there's a real risk for teams that haven't planned ahead. Below we'll cover what Google has said, what teams are seeing, why capacity is tight, and how to protect the capacity you depend on at the lowest price possible.

What Google Has Said

Demand for Google Cloud has never been higher, and it's continuing to accelerate. By the end of June, Google's backlog had grown to $514 billion, and Google is investing aggressively to meet it. CFO Anat Ashkenazi told analysts Google would lean on third-party capacity in Q3 as a bridge while it built its own, accepting lower margins to do it. Alphabet raised its 2026 capital spending forecast to $195 billion to $205 billion. Google has also made a deal to pay SpaceX roughly $920 million a month for computing power.

Some of that demand is coming from the biggest names in tech. The Financial Times reported that Meta and other Google customers wanted even more Gemini capacity than Google had available.

Then came the interview this month. Pichai said Google is prioritizing frontier AI development and plans compute two to three years ahead, so capacity will stay tight into 2027. Analysts read it as a sign of how much demand Google Cloud is seeing.

What Teams Are Seeing

The teams running into these limits are the ones requesting capacity on demand. Planning capacity ahead of time is hard. Reservations are billed whether you use them or not, and usage that shifts hour to hour makes it difficult to know how much to set aside, so many teams default to on-demand and count on it being there when they need it. When a VM or GKE node isn't covered by a reservation, Compute Engine has to find free capacity in the zone at the moment you ask for it. If the zone is busy, the request comes back with ZONE_RESOURCE_POOL_EXHAUSTED. Capacity you've reserved is already set aside for you, so it isn't affected. Google's documentation notes that the error isn't a quota problem, so raising your quota won't help.

Demand isn't limited to GPUs and TPUs. In April, a developer on Google's forum reported getting the error in us-central1 across zones a, b, c and f, on e2, n1 and n2 machine types, and others said they were seeing it there and in us-west2. A separate thread tracks N4D stockouts lasting hours. These are the general-purpose machine types that everyday web services, databases and CI pipelines run on.

AI workloads don't only consume accelerators. They pull CPUs, memory and power along with them, which is why Google's own guidance now lists some CPU-based VM families alongside GPUs and TPUs as scarce resources worth reserving.

GitLab shows what this looks like when deployments depend on scaling up on demand. GitLab.com runs on Google Cloud, and its deployments rely on GKE node pools adding nodes as they roll out. In August, deployments that normally take about 20 minutes per cluster took up to 45 minutes while node pools waited on new nodes, which GitLab's engineers traced to N4D stockouts in us-east1-c.

We've heard from teams running on Google Cloud that they've been running into it more often since early October.

For teams relying on on-demand capacity, the first sign usually shows up on GKE as pods stuck in Pending and nodes that never join the cluster. A stockout isn't an outage, so it won't appear on a status page. It tends to surface as a deploy that takes longer than expected.

Why the Holiday Season Raises the Stakes

If you haven't secured capacity ahead of the holidays, this is when you're most likely to run into a stockout. Traffic peaks, and every other customer in the region is scaling out at the same moment. If the autoscaler can't get nodes then, your customers could see slow pages and dropped transactions.

Google's guidance puts seasonal traffic peaks in the same category as product launches and migrations: predictable demand to secure in advance rather than expect to find on demand. Retail, media, travel and payments companies whose busiest weeks fall between Black Friday and New Year's have the most to gain from planning ahead, along with anything that depends on them, from ad platforms to logistics APIs.

How Reservations and Commitments Protect Capacity

Google gives customers two separate tools to protect capacity without overpaying for it. A reservation holds capacity. A committed use discount (CUD) lowers the price.

A reservation tells Compute Engine to set aside capacity for a specific machine type in a specific zone. Once it's created, that capacity stays available to you until you delete the reservation, whether or not anything is running on it. The catch is billing: Google charges for reserved resources at the same on-demand rate as running VMs for as long as the reservation exists. Reserve 100 VMs and use 60, and you pay for all 100.

A committed use discount is a one- or three-year agreement to a level of usage or spend in exchange for a lower rate. On its own, a CUD doesn't hold any hardware. What makes the two work together is that CUDs apply to reserved capacity the same way they apply to running VMs, so a reservation covered by commitments is billed at the committed rate instead of on-demand. Resource-based commitments can also have reservations attached at purchase, as long as they share the same project, region and machine series.

Google offers a few more options depending on the workload:

Future reservations let you request capacity for a later start date, which suits a holiday peak you can see coming.
Dynamic Workload Scheduler's calendar mode books GPU and TPU capacity for a fixed window, and flex-start mode queues jobs that can wait until hardware frees up.
Spot VMs and Flex-start VMs draw from separate capacity pools and work for batch or fault-tolerant jobs.
GKE custom ComputeClasses and managed instance group instance flexibility let you rank acceptable machine families and zones, so a stockout in one falls through to the next.

The hard part is sizing. Reserve too little and you're back to relying on on-demand during the peak. Reserve too much without enough commitment coverage and you pay on-demand rates for idle capacity. Commit too much and you're locked into one- or three-year spend that outlasts the workload. Getting it right means knowing your steady baseline, your peak and the gap between them, and recalculating as all three change.

What Should You Do Now?

With demand expected to stay high into 2027, the earlier you plan, the better.

Find out whether you're already affected. Search your GKE autoscaler events and Compute Engine audit logs for ZONE_RESOURCE_POOL_EXHAUSTED over the last few months, and note which machine families, zones and services were involved.
Reserve capacity for the services you can't afford to have slow down, using future reservations if it needs to start on a specific date.
Widen fallbacks for everything else by allowing more machine families and zones, and move batch and CI work to Spot or flex-start VMs.
Cover your reserved and steady-state capacity with the right commitments. This step is the hardest to get right. Google offers resource-based CUDs by machine series and region, spend-based CUDs that span machine families, and one- and three-year terms for each. The right combination depends on usage that changes hour to hour.

How nOps Can Help You Navigate the Shortage

Your Google Cloud usage moves by the hour, as autoscalers react to traffic and stockouts push workloads between machine families and zones. For every hour, there is one mix of commitments that would cover it at the lowest price. It has to be chosen from thousands of combinations of commitment type, machine series, region and term, and no team can work that out by hand.

nOps commitment management for Google Cloud does. It analyzes your usage hour by hour, evaluates the full range of commitment options against it, and automatically buys the mix that maximizes your savings. Instead of relying on infrequent bulk purchases, nOps commits in continual, small increments. That creates frequent decision points and expiration opportunities, so your coverage can be fine-tuned up or down as usage changes, keeping savings high while reducing commitment risk. You only pay a percentage of what nOps saves you.

If you'd like to maximize savings and flexibility on Google Cloud, or you no longer want to tactically manage Google Cloud commitments yourself, nOps is offering a free GCP analysis that builds the best commitment strategy for your specific environment and quantifies exactly what it would save you. The process is lightweight, taking only 30 minutes on your end, and gives you the data you need to make an informed decision.

nOps manages over $5 billion in cloud spend and was recently named #1 in G2's cloud cost management category.

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